{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T02:38:27Z","timestamp":1792118307465,"version":"4.4.0"},"reference-count":30,"publisher":"American Medical Association (AMA)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Comprehensive data on the prevalence of various life stressors and their role in posttraumatic stress disorder (PTSD) among women are lacking.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To determine the prevalence of a broad range of life stressors and their association with PTSD in a large nationally representative cohort of women.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This cross-sectional analysis used data from the population-based Stress-And-Gene-Analysis, which invited women in Iceland to complete an online survey from March 1, 2018, to July 1, 2019. Participants were a nationally representative sample of women 18 to 69 years of age. This analysis was conducted from January 21, 2022, to September 13, 2024.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n \n History of 23 life stressors assessed with the Life Events Checklist for the\n Diagnostic and Statistical Manual of Mental Disorders<\/jats:italic>\n (Fifth Edition) (\n DSM-5)<\/jats:italic>\n and additional survey questions.\n <\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n \n The main outcome was probable PTSD during the past month assessed for the worst life stressor experienced as evaluated with the PTSD Checklist for\n DSM-5<\/jats:italic>\n . Modified Poisson log-linear models were used to assess prevalence ratios (PRs) of PTSD by type of life stressor, adjusting for multiple covariates, including number of life stressors and time since the worst life stressor.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Overall, 28\u202f199 women participated in the study (mean [SD] age, 43.8 [13.7] years). The prevalence of life stressors varied substantially, ranging from 3.3% for war or armed conflict\u2013related events to 66.4% for unwanted sexual experiences. The overall prevalence of probable PTSD was 15.9% and varied substantially across life stressor types. The highest prevalence of probable PTSD was for sexual assault (1427 of 3872 [36.9%]) and lowest for natural disasters (15 of 456 [3.3%]). Interpersonal trauma, specifically sexual assault (adjusted PR [APR], 6.66 [95% CI, 4.01-11.04]), other unwanted sexual experience (APR, 4.33 [95% CI, 2.53-7.42]), and physical assault (APR, 4.44 [95% CI, 2.63-7.49]) were associated with the highest prevalence increase of probable PTSD compared with natural disasters as the reference category.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n This cross-sectional study of 28\u202f199 women in Iceland, which ranks highest globally in gender equality, found that sexual and physical assaults were among the most common life stressors reported and were associated with the highest prevalence of probable PTSD. The propensity of such trauma to result in PTSD calls for widespread societal efforts to prevent sexual and physical violence and reduce gender-based disparities in mental health.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2024.49430","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T11:31:30Z","timestamp":1733484690000},"page":"e2449430","source":"Crossref","is-referenced-by-count":6,"title":["Prevalence of Life Stressors and Posttraumatic Stress Disorder Among Women in Iceland"],"prefix":"10.1001","volume":"7","author":[{"given":"Edda Bjork","family":"Thordardottir","sequence":"first","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Song","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 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